Bibliographic record
Abstract
Ing Care was founded with the vision of using technology to improve rehabilitation services for Autism Spectrum Disorders (ASD), in the hope of giving all autistic children access to effective treatment and intervention. In this sense, Ing Care was a social enterprise committed to creating social value by addressing social problems. During the eight years after its founding, Ing Care offered online training courses for autism therapists, and introduced the Verbal Behavior-Milestones Assessment and Placement Program ("VB") to provide patient-specific and individualized rehabilitation training, disrupting traditional practices. However, VR met a lukewarm response, forcing Ing Care to build its own rehabilitation facilities to endorse VB. Moreover, Ing Care designed a rehabilitation curriculum map and a digital platform for curriculum implementation and management, and made these products accessible to other rehabilitation facilities, medical institutions, and parents of autistic children. To grow its business, the company had brought in professional managers, who raised issues of cross-cultural conflict. In spite of this, by early 2022, Ing Care had two lines of business: one was to operate 15 self-owned high-end rehabilitation centers; the other was associated with transferring its capabilities outward (for example by creating the IDEA Inside brand, setting out exemplary models of IDEA teaching, and helping medical institutions to build an autism screening system. While the two business sectors presented both opportunities and challenges, Ing Care's founding team faced a strategic positioning dilemma: As a social startup with limited resources and capabilities, Ing Care had to decide on the relative emphasis it should place on opening more rehabilitation centers versus empowering other industry players to avoid spreading itself too thinly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".